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An ensemble multi-label feature selection algorithm based on information entropy
Joint Authors
Li, Shining
Zhang, Zhenhai
Duan, Jiaqi
Source
The International Arab Journal of Information Technology
Issue
Vol. 11, Issue 4 (31 Jul. 2014)8 p.
Publisher
Publication Date
2014-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In multi-label classification, feature selection is able to remove redundant and irrelevant features, which makes the classifiers faster and improves the prediction performance of the classifiers.
Currently most of feature selection algorithms in multi-label classification are dependent on the concrete classifier, which leads to high computation complexity.
Hence this paper proposes an ensemble multi-label feature selection algorithm based on information entropy (EMFSIE), which is independent on any concrete classifiers.
Its core idea consists of: 1).
we employ the information gain to evaluate the correlation between the feature and the label set ; 2).
to filter out useful features more effectively, we calculate the information gain in an ensemble framework and filter out useful features according to the threshold value determined by the effective factor.
We validate EMFSIE on four datasets from two domains using four different multi-label classifiers.
The experimental results and their analysis show preliminarily that EMFSIE can not only remove more than 70 % of original features, which makes the classifiers faster, but also keep the prediction performance of the classifiers as good as before, even enhance the prediction performance on three datasets under the two-tailed paired t-tests at 0.05 significance level.
American Psychological Association (APA)
Li, Shining& Zhang, Zhenhai& Duan, Jiaqi. 2014. An ensemble multi-label feature selection algorithm based on information entropy. The International Arab Journal of Information Technology،Vol. 11, no. 4.
https://search.emarefa.net/detail/BIM-334388
Modern Language Association (MLA)
Li, Shining…[et al.]. An ensemble multi-label feature selection algorithm based on information entropy. The International Arab Journal of Information Technology Vol. 11, no. 4 (Jul. 2014).
https://search.emarefa.net/detail/BIM-334388
American Medical Association (AMA)
Li, Shining& Zhang, Zhenhai& Duan, Jiaqi. An ensemble multi-label feature selection algorithm based on information entropy. The International Arab Journal of Information Technology. 2014. Vol. 11, no. 4.
https://search.emarefa.net/detail/BIM-334388
Data Type
Journal Articles
Language
English
Notes
Includes appendix.
Record ID
BIM-334388